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Google Calendar Apps Employs Machine Learning

Good -- or perhaps bad -- news for procrastinators: Google Calendar is now using machine learning to keep you more closely on schedule.
6 Machine Learning, AI, Analytics Trends To Watch
6 Machine Learning, AI, Analytics Trends To Watch
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Google has employed machine learning on its Calendar application in an effort to help its users better keep track of, and complete, long-term goals.

Users simply add a personal goal -- like hitting the gym three times a week, for example -- and Google Calendar will help them find the time and stick to it.

Setting a goal requires the user to answer a few questions, specifying duration and times. From there Calendar will look at the user's schedule and find the best windows to schedule time to help complete the goal.

It's another example of a major technology company using machine learning -- the concept of pattern recognition and computational learning theory -- to make its users' lives easier.

The Calendar feature also incorporates elements of behavioral analytics and sentiment analysis by subtly encouraging users to keep to a schedule they might otherwise avoid.

It could be regarded as a blessing or a curse for serial procrastinators.

Among other features, Calendar will automatically reschedule if the user adds another event that's in direct conflict with a goal, and allows the user defer a goal at any time.

But don't worry -- Calendar will make time for it later.

"Calendars should help you make the most of your time -- not just be tools to track events," Jyoti Ramnath, a product manager at Google, wrote in an April 12 blog post. "Whether it's reading more books, learning a new language or working out regularly, achieving your goals can be really hard."

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Google is investing in machine learning on a much bigger scale than Calendar applications. In March, the company announced the alpha release of Cloud Machine Learning, a framework for building and training custom data models using distributed learning algorithms based on its TensorFlow machine learning framework.

In addition to Google, Facebook, Salesforce, Microsoft, and numerous other tech titans are investing rapidly in artificial intelligence (AI) specialists and machine learning platforms.

Salesforce has quietly been amassing talent in the artificial intelligence domain, most recently with the acquisition of MetaMind, a company working on deep learning for automated image recognition, earlier this month.

IBM's Watson landed a job with global tax auditing and advisory firm KPMG, which aims to use Watson's cognitive computing capabilities to look at volumes of data that, from a human standpoint, would be impossible to manage.

According to an October report from Gartner, machine learning could give rise to a spectrum of smart machine implementations, including robots, autonomous vehicles, virtual personal assistants (VPAs), and smart advisors, all of which act in an autonomous or a semiautonomous manner.

"Over the next five years we will evolve to a postapp world with intelligent agents delivering dynamic and contextual actions and interfaces," David Cearley, vice president and Gartner Fellow, wrote in the report. "IT leaders should explore how they can use autonomous things and agents to augment human activity and free people for work that only people can do. However, they must recognize that smart agents and things are a long-term phenomenon that will continually evolve and expand their uses for the next 20 years."